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%2C since models trained on popular well-suited datasets are susceptible to adversarial attacks%2C allowing subtle input interventions to mislead the model. In this work%2C we validate the usage of natural language explanation as a model-agnostic defence strategy through extensive experimentation: only by fine-tuning a classifier on the explanation rather than premise-hypothesis inputs%2C robustness under various adversarial attacks is achieved in comparison to explanation-free baselines. Moreover%2C since there is no standard strategy of testing the semantic validity of the generated explanations%2C we research the correlation of widely used language generation metrics with human perception%2C in order for them to serve as a proxy towards robust NLI models. Our approach is resource-efficient and reproducible without significant computational limitations.?quality=80&w=800)
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